Projected Life Expectancy for Adolescents With HIV in the US

Key Points Question How does life expectancy compare for 18-year-old youth who acquired HIV perinatally (PHIV), youth who acquired HIV nonperinatally (NPHIV), and youth without HIV in the US? Findings In an adolescent-specific microsimulation model, youth with PHIV and youth with NPHIV had lower life expectancy than youth without HIV; youth with NPHIV had lower life expectancy compared with youth with PHIV. The life expectancy gap between youth with PHIV and youth without HIV decreased in an ideal HIV care scenario, but persisted for youth with NPHIV. Meaning The study results suggest that interventions focused on HIV care and the social factors associated with disparities in non–HIV-related mortality are needed to improve life expectancy for youth with HIV in the US.


Distribution of race/ethnicity among modeled cohorts
The race/ethnicity distribution of the cohorts was informed by United States (US) Centers for Disease Control and Prevention (CDC) data among youth with HIV (YHIV) ages 13 to 24 years (Manuscript Table 1).We used the same distribution of race/ethnicity for both youth with HIV acquired perinatally (YPHIV) and youth with HIV acquired non-perinatally (YNPHIV), which have similar reported race/ethnicity in national cohort datasets (ATN, IMPAACT, HIV Research Network), as well as the CDC data (eTable 1) 2, 14-15 .

Mortality input derivation and validation overview
HIV-related and non-HIV-related mortality were distinguished in model inputs.Risk for HIVrelated mortality depended on age, CD4 count and antiretroviral status while risk for non-HIVrelated mortality depended on age, sex, race/ethnicity, and risk for HIV acquisition.

Adolescent Medicine Trial Network for HIV/AIDS Intervention (ATN) and International Maternal
Pediatric Adolescent AIDS Clinical Trials (IMPAACT) Network 15, 50 data were used to derive monthly probabilities of HIV-related mortality separately for YPHIV and YNPHIV from ages 18 to <30 years ("adolescent" inputs, eTable 3).Multicenter AIDS Cohort Study (MACS) 21 and HIV-CAUSAL Collaboration 22 data informed off-ART HIV-related mortality probabilities from age 30 years onward; published North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) data [23][24] informed on-ART HIV-related mortality probabilities from age 30 years onward ("adult" inputs, Manuscript Table 1).Overall adult HIV-related mortality was calibrated to data from the CDC 37 .
We calculated monthly non-HIV-related mortality probabilities using a life table approach.We calculated sex-stratified HIV-deleted life tables as described elsewhere 51 , using cause-specific © 2024 Neilan AM et al.JAMA Health Forum.mortality data 29 , and population size estimates by age and sex 30,52 (eTable 10).To generate monthly race/ethnicity-adjusted non-HIV-related mortality probabilities, we calculated race/ethnicity-specific HIV-deleted life tables and weighted by the race/ethnicity distribution among the YHIV cohorts.These race/ethnicity-adjusted, sex-stratified mortality rates were then adjusted by risk for HIV acquisition by applying risk factor-specific relative mortality ratios (RMRs) weighted by the distribution of risk for HIV acquisition among YPHIV and YNPHIV, respectively.Categories for risk for HIV acquisition included men who have sex with men (MSM), heterosexually active individuals at increased risk for HIV infection, and people who ever injected drugs (PWID).Because we found limited data to inform non-HIV-related mortality among heterosexually active individuals at increased risk for HIV infection, we used non-HIVrelated mortality among individuals with incomes below the federal poverty level as a proxy 16,53 .

Adolescent input derivation
To derive adolescent HIV-related mortality model inputs, we used age-, CD4-and viral-load-/antiretroviral-therapy-stratified ATN and IMPAACT data 15, 50 .In the ATN and IMPAACT datasets, we defined HIV-related mortality as that attributed to CDC or World Health Organization (WHO)-defined HIV-related events.We also included suicide in this category.The observed mortality rates in the original ATN/IMPAACT data can be found in eTable 4.
Given that mortality events were not observed within some age, CD4 count, and viral load/antiretroviral therapy (ART) strata in the ATN and IMPAACT data due to limited persontime of follow-up in the data, we used a generalized linear regression model with a log gamma distribution in SAS Software 53 to obtain predicted mortality rates by age, CD4, and viral load/ART strata which were then converted to monthly probabilities of adolescent HIV-related mortality for the microsimulation model.These regression-estimated inputs were then calibrated to the overall mortality rate observed in the ATN and IMPAACT data by using the ratio of the overall mortality rate calculated from the ATN and IMPAACT data/the overall mortality rate observed from the regression model.This ratio was used to consistently scale down the regression-estimated age-, CD4-, and viral-load-/ART-stratified inputs to produce the final derived inputs for adolescent HIV-related mortality (eTable 3).

Adolescent input validation
We then assessed the difference between microsimulation-model-projected HIV-related mortality rates and the source data from which it was derived 54 .We ran the simulation and extracted person-time for each relevant age group (18-24 and 25-30 years) stratified by CD4 and current ART use (eTable 5).
Then, we multiplied these percentages in eTable 5 by the corresponding mortality probabilities (eTable 3) to calculate an "expected" HIV-related mortality rate.Finally, we compared that expected rate to the microsimulation-model-projected HIV-related mortality rate from the same stratum using the ratio between the two values (such that a ratio equal to 1 would represent perfect agreement between the mortality rates).We also performed the same calculations using the original, observed mortality rates from the ATN/IMPAACT data (eTable 4), to assess the differences between the final model inputs and the observed mortality rates (eTable 6).
The modeled HIV-related mortality rates varied from the expected HIV-related mortality rates by a margin of ~1% or less and fell within the 95% confidence interval (CI) bounds of the observed HIV-related mortality rates from the ATN and IMPAACT data (rightmost column of eTable 6).

Adult input derivation
To calculate adult HIV-related mortality, we used MACS 21 and HIV-CAUSAL 22 data for off-ART mortality rates stratified by CD4 count.We used the mortality ratio comparing each CD4 strata to the CD4 <50 strata from the MACS dataset and scaled the off-ART HIV-related mortality rate to the HIV-CAUSAL off-ART mortality rate to reflect the change in mortality in the current era.
We used data from published NA-ACCORD studies to calculate on-ART mortality rates by CD4 cell count, which is based on patients who entered HIV care between 2011 and 2017 [23][24] .

Adult input validation
We again ran the microsimulation model and extracted person-time for the 'adult' portion of the model (age >30 years) stratified by CD4 and current ART use (eTable 7).
Because the adult HIV-related mortality consists of both opportunistic-infection (OI)-related mortality and 'chronic' HIV mortality (i.e., HIV-related mortality of non-OI causes), we adjusted these percentages to account for the expected amount of time that an individual had an opportunistic infection within each CD4 strata.Then we multiplied these percentages by the corresponding mortality probabilities (Manuscript Table 1) to calculate an "expected" HIV-related mortality rate.Finally, we compared that expected rate to the modeled HIV-related mortality rate for ages >30 years using the ratio between the two values (such that a ratio equal to 1 would represent perfect agreement between the mortality rates) (eTable 8).The microsimulation modeled HIV-related mortality rates varied from the expected HIV related mortality rates by a margin of ~1% or less.
After validating our inputs, we compared our microsimulation-model-projected HIV-related mortality rate to an external source to assess the magnitude of the difference and assess face validity.To do this, we extracted the modeled chronic HIV-related mortality (as in eTable 8), this time combining it with the modeled OI-related mortality to find the overall HIV-related mortality.
We then compared this to the HIV-related mortality reported by the CDC 37 , and calculated the absolute and percent difference in the values (eTable 9).The modeled HIV-related mortality rate was lower than the CDC-reported rate.The absolute difference in rates would represent a total difference of around 1,000 HIV-related deaths annually in the US out of approximately 16,000 annual all-cause deaths among 1.2 million people with HIV in the US 37 .

Relative mortality ratios
We used cause-specific mortality data 29 , and population size estimates by age, sex, and race/ethnicity 30,52 to estimate race/ethnicity-specific mortality rates and average population mortality rates.Subsequently, we calculated the RMRs stratified by race/ethnicity by comparing the mortality rate observed in each race to the average population mortality rate (eTable 11).
We used publicly available data on mortality from the 2001 to 2014 National Health and Nutrition Examination Surveys (NHANES) 44 and their Linked Mortality Files (LMF) 55 to generate RMRs for the HIV acquisition risk categories.LMFs link survey data from NHANES with death certificate records from the National Death Index.We then derived RMRs stratified by risk for HIV acquisition by comparing observed mortality of various risk for HIV acquisition cohorts to the mortality of those without the risk category (eTable 12).The corresponding RMRs were then applied to the baseline monthly non-HIV-related mortality rates from US Life Tables to adjust for race/ethnicity and risk for HIV acquisition in our modeled cohorts.Once RMRs were calculated that factored in age, sex, race/ethnicity, and risk factor group, they were combined with the baseline non-HIV-related mortality using the relevant distribution of individuals to reach the final mortality probability at any age for any given person in the model.

Age-adjusted relative mortality ratios
To reflect age-related changes in non-HIV-related mortality risks related to being assigned to one of the CDC-defined risk for HIV acquisition categories (MSM, heterosexually active individuals at increased risk for HIV infection, or PWID), we derived RMRs separately for individuals below and over 45 years of age for each of these categories, for both the risk for HIV acquisition and race-stratified RMRs.This age threshold generally resulted in lower RMRs and consequently lower mortality rates on average for individuals older than 45 years (Tables S11   and S12).
In some cases, the age threshold resulted in significantly lower mortality rates for individuals older than 45 years.For example, PWID had significantly lower mortality rates after age 45 years compared to before age 45 years.This is a result of the lower incidence of drug use at older ages combined with the survivor bias among by those who lived to that point.Due to the high rates of premature mortality that PWID experience, there is a difference in survival before and after age 45 years.This change in survival after age 45 years is visible from the inflection points in the projected survival curves for PWID (Manuscript Figure 3).We conducted sensitivity analyses, using age 55 years as the threshold and eliminating an age threshold altogether.The inflection in the survival curve remained when we used age 55 years as the threshold and was eliminated when we eliminated the age threshold (eFigure 4).However, we elected to use age 45 years to ensure that there was adequate sample size in both age strata.The overall results remained consistent across these sensitivity analyses, with the PWID cohort having the lowest projected survival compared to other risk cohorts.

Socioeconomic status as a proxy for HIV acquisition risk among heterosexually active individuals
Because there is no single method to ascertain which heterosexually active individuals are at increased risk for HIV infection, the initial cycle of the National HIV Behavioral Surveillance system 56 conducted a pilot study using venue-based sampling and respondent-driven sampling to determine an optimal sampling strategy 57 .This initial study, based on 2006-2007 data, found an association between socioeconomic status and HIV prevalence, postulating that adverse social conditions associated with economic status could increase the risk of HIV infection including sexual exploitation, unstable sexual partnerships, limited access to health care and preventive services and socioeconomic segregation confining individuals to sexual networks with high underlying rates of HIV and other sexually transmitted infections.
Since that time, in each of its reporting cycles (2007, 2010, 2013, 2016 and 2019), the National HIV Behavioral Surveillance report has collected data among heterosexually active adults at increased risk for HIV infection, using socioeconomic measures as a marker of increased risk for HIV.The most recent 2019 report inclusion criteria included participants classified as low income (instead of participants classified as low socioeconomic status), defined as achieving no more than high school education or having income at or below the federal poverty level, calculated by the Health and Human Services (HHS) poverty guidelines 16 .This change was made to account for substantial differences in cost of living across cities.
We defined low socioeconomic status as those with poverty income ratio (PIR) less than one.
The PIR is the midpoint of family income divided by the calendar-year-specific poverty threshold defined by the US Census Bureau 25 .
). Relative mortality ratios, stratified by sex, age, and risk for HIV acquisition Base-case life expectancy and life expectancy loss, stratified by sex assigned at birth, modeled cohort, and risk for HIV acquisition Monthly probabilities of non-HIV-related mortality of modeled cohorts of heterosexually active individuals at increased risk for HIV infection, by age and sex This table contains base-case non-HIV-related mortality for modeled cohorts of heterosexually active individuals with increased risk for HIV infection.It was calculated using the race/ethnicityadjusted non-HIV-mortality, and the relative mortality ratios for individuals with incomes below the calendar year-specific federal poverty level calculated from National Health and Nutrition Examination Survey data25.We used this mortality as a proxy for the mortality of heterosexually active individuals increased risk for HIV infection.Monthly probabilities of non-HIV-related mortality of modeled cohorts of people who ever injected drugs, by age and sex © 2024 Neilan AM et al.JAMA Health Forum.ATN: Adolescent Medicine Trials Network for HIV/AIDS Interventions; CDC: Centers for Disease Control and Prevention; IMPAACT: International Maternal Pediatric Adolescent AIDS Clinical Trial Network; YHIV: youth with HIV; YNPHIV: youth with HIV acquired nonperinatally; YPHIV: youth with HIV acquired perinatally.ART: antiretroviral therapy; RNA: ribonucleic acid; YNPHIV: youth with HIV acquired non-perinatally; YPHIV: youth with HIV acquired perinatally.ATN: Adolescent Medicine Trials Network for HIV/AIDS Interventions; IMPAACT: International Maternal Pediatric Adolescent AIDS Clinical Trials Network; YNPHIV: youth with HIV acquired nonperinatally; YPHIV: youth with HIV acquired perinatally y: years.©2024Neilan AM et al.JAMA Health Forum.ATN: Adolescent Medicine Trials Network for HIV/AIDS Treatment; CI: confidence interval; IMPAACT: International Maternal Pediatric Adolescent AIDS Clinical Trial network; PY: person-years; YNPHIV: youth with HIV acquired non-perinatally; YPHIV: youth with HIV acquired perinatally y: years.ART: antiretroviral therapy; YNPHIV: youth with HIV acquired non-perinatally; YPHIV: youth with HIV acquired perinatally.*Basedonfinal inputs using regression models derived from ATN/IMPAACT data.†Theserateswere for the 25-<35-year-old age stratum in the original dataset.ATN: Adolescent Medicine Trials Network for HIV/AIDS Interventions; IMPAACT: International Maternal Pediatric Adolescent AIDS Clinical Trials Network; PY: person-years; YNPHIV: youth with HIV acquired non-perinatally; YPHIV: youth with HIV acquired perinatally.ART: antiretroviral therapy; YNPHIV: youth with HIV acquired non-perinatally; YPHIV: youth with HIV acquired perinatally.Y: years © 2024 Neilan AM et al.JAMA Health Forum.eTable12.© 2024 Neilan AM et al.JAMA Health Forum.eTable 13.MSM: men who have sex with men; YNPHIV: youth with HIV acquired non-perinatally; YPHIV: youth with HIV acquired perinatally; YWoH: youth without HIV.eTable 17.YNPHIV: youth with HIV acquired non-perinatally; YPHIV: youth with HIV acquired perinatally; YWoH: youth without HIV.eTable 18. NHANES: National Health and Nutrition Examination Survey; PWID: people who ever injected drugs; YNPHIV: youth with HIV acquired non-perinatally; YPHIV: youth with HIV acquired perinatally; YWoH: youth without HIV.
Projected survival from age 18 years, by sex, and age cutoffs for relative mortality ratios for HIV acquisition risk a. Male b.Female Ref: reference group; YNPHIV: youth with HIV acquired non-perinatally; YPHIV: youth with HIV acquired perinatally; Yrs: years; YWoH: youth without HIV.